Pretrained Model

Pretrained models represent a paradigm shift in machine learning, focusing on training general-purpose models on massive datasets before adapting them to specific tasks. Current research emphasizes efficient adaptation techniques like multi-task fine-tuning and knowledge distillation, often employing transformer-based architectures or variations of convolutional neural networks, to improve performance and reduce training costs. This approach significantly impacts various fields, from accelerating medical diagnosis through improved image analysis and natural language processing to optimizing database query execution and enhancing code generation. The resulting models offer improved efficiency and accuracy across diverse applications compared to training from scratch.